import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
fig, ax = plt.subplots(figsize=(13,8))
ax.plot(stocks['date'],stocks['GOOG'])
x_ticks=range(0,stocks.shape[0],20)
ax.set_title('Google stock')
ax.set_xlabel('date')
ax.set_ylabel('stock value')
ax.set_xticks(x_ticks)
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
fig,ax=plt.subplots(figsize=(13,8))
x_ticks=range(0,stocks.shape[0],20)
ax.set_xticks(x_ticks)
ax.plot(stocks['date'],stocks['GOOG'],label='GOOG')
ax.plot(stocks['date'],stocks['AAPL'],label='AAPL')
ax.plot(stocks['date'],stocks['AMZN'],label='AMZN')
ax.plot(stocks['date'],stocks['FB'],label='FB')
ax.plot(stocks['date'],stocks['NFLX'],label='NFLX')
ax.plot(stocks['date'],stocks['MSFT'],label='MSFT')
plt.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
fig,ax=plt.subplots(figsize=(13,8))
ax=sns.scatterplot(x='total_bill',y='tip',hue='sex',data=tips)
plt.show()
I think when the total_bill in a low level, there's nothing difference between male and female when it comes to giving tips. But when the total_bill is in the high level, male give more tips than female.
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# YOUR CODE HERE
df = px.data.stocks()
fig=px.line(df,x='date',y=['GOOG','AAPL','AMZN','FB','NFLX','MSFT'],markers=True)
fig.show()
tips = sns.load_dataset('tips')
fig=px.scatter(tips,x='total_bill',y='tip',color='sex',facet_col="smoker",facet_row="time")
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# YOUR CODE HERE
df=px.data.gapminder()
df_2007=df.query('year==2007')
df_2007_new=df_2007.groupby('continent').sum()
fig=px.bar(df_2007_new,x='pop',y=df_2007_new.index,orientation='h',text_auto=True,color=df_2007_new.index)
fig=fig.update_yaxes(categoryorder='total ascending')
fig.show()